Metabolite analysis – Metabolomics
non-plant
(mostly bacterial & medical)
plant-specific
Metabolomics, spring 06
Hans Bohnert
ERML 196
bohnerth@life.uiuc.edu
265-5475
333-5574
http://www.life.uiuc.edu/bohnert/
Technology in a Nutshell (six steps)
• Extraction of metabolites
comprehensive, avoid degradation,
avoid modification (Fiehn et al. 2000; Kopka et al. 2004)
• Derivatization
make amenable for GC (volatile but temperature
stable)
(Schmelz et al. 2004)
• Separation by GC
standardized gas flow, automation,
temperature programming, capillary
column choice
(1) Some more on
instrumentation
and basics –
technology in a nutshell
with a focus
on GC/MS
(2) Challenges
(3) Literature
(4) Integration possibilities
• Ionization
ESI, MALDI, EI (electron impact) - most prevalent
(least susceptible to suppression, reproducible)
• Time resolved detection of fragments/molecules
(dependent on analytical objective) (Ryan et al. 2004)
different mass detection devices
(Mueller et al. 2002)
sector-field detector
quadrupole detector (QUAD) – routine work
ion trap detectors – allows for MS-MS, 2D detection
time-of-flight (TOF) – fast scans or precision mass
> Ideal: GCxGC-TOF-MS
• Acquisition of data and evaluation
the real challenge
(3/28/06)
Extraction, Derivatization, Chromatography
• Metabolite concentrations change rapidly, within seconds
in primary metabolism – rapid sampling
• Metabolite composition changes during freeze storage –
keep extracts, not tissues
• Metabolite amounts can be highly variable in individuals –
large pools or many inividuals, lots of repeats
• Metabolites are highly dynamic – take samples diurnally,
different leaf age, different developmental age
• Extracts from methanol-water/chloroform phases
• Alkoxyamination – CH3-O-NH2 > stabilize C=O
• Silylation (mono-/di-/tri-methyl-silyl), wide spectrum •Si(CH3)3 (TMS)
• Alkylation – mostly methylations, transalkylation of esterbonds > efficient breakdown of complex metabolites
• Acylation, less reactive – acetylation or trifluoro-acetylation
• Separation of volatiles in GC columns – choice of column
Mass detection and quantitative calibration techniques
Kopka J (2006) GC-MS. In: Plant Metabolomics (Saito, Dixon, Willmitzer, eds.), Springer, pp 3-20.
Mass spectral deconvolution of deuterated mass isotopomers
Mass spectral deconvolution of deuterated mass isotopomers
Compound Resolution - GC/MS instruments
glycerol
malic
glycine
polar phase (methanol/water)
glucose
G1-P
inositol
sucrose
oleic
stachyose
Reality of complexity vs. reality of knowledge
Extraction scheme
Weckwerth, 2003.
“Metabolomics in
Systems Biology”
metabolites
proteins
RNA
GC-MS for metabolite profiling
Agilent 5975
inert MSD
Waters
Micromass
GCT
Ionization techniques for GC
• Electron Impact (EI) (-/+)
library searchable spectra, fragmentation, most versatile
• Chemical Ionisation (CI+/-)
molecular weight information
• Desorption Chemical Ionisation (DCI)
thermally labile compounds, molecular weight information
• Field Ionisation (FI) / Field Desorption
soft ionisation, molecular weight information, reduced
background
Ionisation Methods
Matrix
Assisted
Laser
Desorption
Ionisation
The sample is embedded
in solid phase (MATRIX).
MALDI is a mild ionisation
that typically results in
single charged ions, i.e.
the m/z = m/1, and hence
shows the true mass.
Ionisation Methods
pressure / potential gradient
multiple
droplet
division
Taylor cone
- - - +
+
- - + +
+
+
- + +
+
+
+ +
+
+ + + +
+
+ + +
- - - - +
-
+
++
+
+ +
++
+
+ +
++
+
+ +
+
+ +
+
+
+
+
+
+
+ +
+
+ +
+ +
+
[M+nH]n+
+
+
1st generation
droplets
+ kV
+
+ +
ElectroSpray
Ionisation
2nd generation
droplets
(15% charge,
2% mass)
may be
coupled
with LC
The sample is in liquid phase and ESI typically
results in multiple charged ions. This facilitates the
analysis of high mass molecules. However, the true
mass depends on resolution
Ionisation Methods
• Ionisation via
bombardment of
the sample with a
stream of high
energy electrons
• Impact of the high
energy electrons
with the vaporised
sample molecules
causes ejection of
(multiple) electrons
from the analyte
and a radical cation
M+• is formed
Electron Impact
M + e-  M+• + 2e-
Mass analyzers
Quadrupole
Consists of 4 metal
rods to which an
electro-magnetic field
is applied. The
modulation of the
electromagnetic field
only transmits ions
that have a certain
m/z. Quadrupole is a
low resolution mass
filter often used with
ESI.
Analyzers for MS/MS - Triple Quadrupole
Q1 collision Q2
cell
Best combined with an upstream separation device, e.g.,
liquid chromatography or capillary electrophoresis
Mass analyzers
Ionisation
of peptides
Ion acceleration
by high voltage
Time
Of
Flight
Field free drift region
Detection of ions
For GC
or LC
The time needed for an accelerated ion to transverse
a field-free drift zone is directly related to the mass of
an ion / peptide. The longer the flight path the better
the resolution.
Mass analyzers
Magnetic Sector
Analytes deviate in their path based on mass in the magnetic field
of the analyzer. The analyzer focuses a given m/z to the detector.
2D GC-ToFMS
Tandem MS (MS/MS)
58.2
134.6
178.8
MS/MS instruments select a
single ion from a spectrum
obtained by MS1
121.2
primary scan
This ion is fragmented by
collision with an inert gas
The mass of the secondary
fragment ions is measured
by MS2. For peptides, the
amino acid sequence is
deduced from the mass
differences of the ions
121.2
178.8
daughter ion scan
134.6
58.2
Tandem Mass Spectrometry
S#: 1707 RT: 54.44 AV: 1 NL: 2.41E7
F: + c Full ms [ 300.00 - 2000.00]
RT: 0.01 - 80.02
100
90
80
1409
LC
NL:
1.52E8
1991
1615
2149
1621
1411
2147
1387
60
1593
1995
1655
1435
50
1987
1445
1661
40
2001 2177
1937
1779
30
2155
2205
2135
2017
1095
80
75
70
65
60
55
801.0
50
45
40
35
Scan 1707
638.9
25
2207
1105
85
30
1307 1313
20
MS1
90
Relative Abundance
70
95
Base Peak F: +
c Full ms [
300.00 2000.00]
1611
Relative Abundance
638.0
100
1389
2329
872.3
1275.3
15
1707
687.6
10
2331
10
1173.8
20
944.7
783.3
1048.3
1212.0
1413.9
1617.7
1400
1600
1742.1
1884.5
5
0
200
0
5
10
15
20
25
30
35
40 45
Time (min)
50
55
60
65
70
75
400
600
800
1000
m/z
1200
1800
2000
80
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6
T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
850.3
100
95
687.3
90
85
Ion
Source
588.1
80
75
70
MS/MS
65
Relative Abundance
collision
MS-2
MS-1
cell
60
55
851.4
425.0
50
45
949.4
40
326.0
35
524.9
30
25
20
Scan 1708
589.2
226.9
1048.6
1049.6
397.1
489.1
15
10
629.0
5
0
200
400
600
800
1000
m/z
1200
1400
1600
1800
2000
Analyzers: Quadrupole vs. ToF
Quadrupole
- poor resolution
ToF
- high resolution
- better peak
separation
accurate mass
by ToF
Elemental Composition Report
Mass
Calc. Mass mDa
29.0027 29.0027
29.0140
29.0265
29.0391
0.0
-11.3
-23.8
-36.4
ppm
-1.4
-388.7
-822.3
-1255.9
Formula
CHO
H N2
C H3 N
C2 H5
ToF: resolves co-eluting compounds
Peak finding
software
- mass spectral
deconvolution
(further resolves coeluting
and/or low abundant
analytes)
2D GC-MS
Linear dynamic range: 104-106
1D GC
- Analytes Coelute in
complex samples
2D GC
- separates coeluting
peaks in 2nd dimension
Spectral comparison with libraries
NIST, Wiley
chromatogram
Selected peak
Mass-spectrum
Spectral match
Library hits
Comparison of EI and FI spectra
13
56
74.04
87.05
100
56
detective work
56
%
43
EI+
143.11
75.04
55.05
Fragmentation
298.29
255.23
31
199.17
101.06
129.09
157.12
185.16
213.19
241.22
267.27
269.25
299.29
0
298.29
100
12
Intact ion
FI+
Methyl Stearate
CH3(CH2)16COOCH3
%
299.30
300.31
0
m/z
60
80
100
120
140
160
180
200
220
240
260
280
300
GC/MS – a routine technology -
Challenges
(1) Automation of sample preparation, wet chemistry, data processing after
an increasing number of data is obtained,
(2) Extension of the analytical scope – e.g., combined analyses of a sample
using multiple platforms,
(3) Combined analyses with proteome and transcriptome studies
(4) Profiling trace compounds, or signaling molecules in the presence of
(very) abundant ‘bulk’ metabolites,
(5) Increasing accuracy in multi-parallel metabolite quantification
(6) Combining metabolite and flux analyses
(7) Establishing quantitative repeatability, arrive with an unambiguous
nomenclature,
(8) Comparability between analytical platforms, and of work done by
different labs.
References (see slide 1-2)
Birkemeyer et al. (2005) Metabolome analysis: the potential of in vivo labeling with stable
isotopes for metabolite profiling. Trends Biotechnol. 23, 28-33.
Fiehn et al. (2000a) Identification of uncommon plant metabolites based on calculation
of elemental compositions using GC and quadrupole MS. Analyt. Chem.
72, 3573-3580.
Fiehn et al. (2000b) Metabolite profiling for plant functional genomics.
Nat. Biotechnol. 18, 1157-1161.
Fiehn (2003) Metabolic networks of Cucurbita maxima phloem. Phytochem. 62, 875-886.
Kopka et al. (2004) Metabolite profiling in plant biology- platforms and destinations.
Genome Biol. 5, 109-117.
Mueller et al. (2002) A multiplex GC-MS/MS technique for the sensitive and quantitative
single-run analysis of acidic phytohormones and related compounds, and its
application to Arabidopsis thaliana. Planta 216, 44-56.
Roessner-Tunali et al. (2004) Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexokinase phosphorylation diminishes fruit development. Plant Physiol. 133, 84-99.
Ryan et al. (2004) Analysis of roasted coffee bean volatiles by comprehensive twodimensional GC-TOF-MS. J. Chromatogr. A 1054, 57-65.
Schauer et al. (2005) GC-MS libraries for the rapid identification of metabolites in complex
biological samples. FEBS Lett. 579, 1332-1337.
Schmelz et al. (2004) The use of vapor phase extraction in metabolic profiling of
phytohormones and other metabolites. Plant J. 39, 790-808.
Weckwerth et al. (2004) Process for the integrated extraction, identification and
quantification of metabolites, proteins and RNA to reveal their co-regulation
in biochemical networks. Proteomics 4, 78-83.
Some metabolites are very abundant –
how to quantify,
and how to analyze low abundance
(a) Typical ES- mass spectrum for
polar extract green tomato
(L. esculentum) fruit.
Major identifiable peaks:
179 (hexose sugars, [M)H])),
191 (citric/iso-citric acid, [M)H])),
215 (hexose sugars, [M+Cl])),
237 (HEPES buffer, [M)H])),
475 (HEPES buffer, [2M)H])).
(b) Typical ES+ mass spectrum for
polar extract of green tomato
(L. esculentum) fruit.
Major identifiable peaks:
147 (glutamic acid, [M+H]+),
203 (hexose sugars [M+Na]+),
219 (hexose sugars, [M+K]+),
239 (HEPES buffer, [M+H]+),
261 (HEPES buffer, [M+Na]+),
277 (HEPES buffer, [M+K]+).
Dunn et al. (2005) Evaluation of automated
electrospray-TOF MS for metabolic
fingerprinting of the plant metabolome.
Metabolomics 1, 137.
Quantification
Relationship between concentration of
metabolite standard added to a plant
extract and molecular ion intensity.
(a) ES-;
open circle - pyruvate,
open triangle - oxalate,
closed circle - fumarate,
open triangle - oxalate,
closed square - malate,
open diamond - ascorbate.
(b) ES+;
open circle - alanine,
open diamond - proline,
closed triangle - GABA,
closed diamond - aspartate,
closed square - leucine.
Analytical and Biological Variations
Peak intensity for 13
selected metabolite
ions measured in each
of three fruit extracts
of two tomato species
Lycopersicon esculentum - white fill; L. pennellii - grey fill;
1 malic acid,
2 citric acid,
4 C4 sugars,
5 hexoses,
7 fumaric acid,
8 ascorbic acid,
10 leucine/isoleucine, 11 asparagine,
3 GABA,
6 pyruvic acid,
9 valine,
12 glutamine, 13 tyrosine.
For clarity, the responses for 3–8 are increased by a factor of 10, and
those for 9–13 increased by a factor of 50. Values are ion intensity (cps),
calculations employed the summed ion intensity for 180 scans and are
presented as the means of three replicate extracts ± standard deviation.
the gold standard
FTICR-MS (or FT-MS)
Ultra-high resolution - Ultra-high mass accuracy
Metabolomics as a component of “Systems Biology” (SB)
Wang QZ, Wu CY, Chen T, Chen X, Zhao XM. (2006) Integrating metabolomics
into a systems biology framework to exploit metabolic complexity: strategies and
applications in microorganisms. Appl Microbiol Biotechnol. 70: 151-161.
Glinski M, Weckwerth W. (2006) The role of mass spectrometry in plant systems biology.
Mass Spectrom Rev. 2006 Mar-Apr;25(2):173-214.
Oksman-Caldentey KM, Saito K. (2005) Integrating genomics and metabolomics for
engineering plant metabolic pathways. Curr Opin Biotechnol. 16: 174-179.
Goodacre R. (2005) Making sense of the metabolome using evolutionary computation:
seeing the wood with the trees. J Exp Bot. 56: 245-254.
Nikiforova VJ, Gakiere B, Kempa S, Adamik M, Willmitzer L, Hesse H, Hoefgen R. (2004)
Towards dissecting nutrient metabolism in plants: a systems biology case study on
sulphur metabolism. J Exp Bot. 55: 1861-1870.
Kell DB. (2004) Metabolomics and systems biology: making sense of the soup. Curr
Opin Microbiol. 7: 296-307.
The next 2 slides indicate that not even in yeast SB metabolomics is included.
The slides are from this website for which the library has a trial period:
http://www.mrw.interscience.wiley.com/ggpb/articles/g204307/frame.html
Functional Genomics in Saccharomyces cerevisiae
No Metabolites
Dolinski & Troyanskaya, 2006
A structure for the Bayesian network in MAGIC. The network is instantiated with
evidence (at the bottom nodes) for each pair of genes in the yeast genome, and
the final confidence level is produced on the basis of the evidence for biological
relationship available for each pair of genes and on the prior probabilities encoded
in the network conditional probability tables (Troyanskaya et al. 2003).
Sources of functional genomics data collections for S. cerevisiae
GRID
Breitkreutz et al. (2003)
Genet./phys. Interactions http://biodata.mshri.on.ca/yeast_grid/servlet/SearchPage\
BIND
Bader et al. (2003)
Genet. interact., pathwys http://www.blueprint.org/bind/bind.php
DIP
Xenarios et al. (2002)
Physical interactions
http://dip.doe-mbi.ucla.edu/dip/Main.cgi
MINT
Zanzoni et al. (2002)
Physical interactions
http://160.80.34.4/mint/
IntAct
Hermjakob et al. (2004b)
Physical interactions
http://www.ebi.ac.uk/intact/index.html
Deletion Consortium
Winzeler et al. (1999); Giaever et al. (2002)
Large-scale phenotype analysis
http://www-sequence.stanford.edu/group/yeast_deletion_project/data_sets.html
GEO
Edgar et al. (2002); Brazma et al. (2003)
MicroArray
http://www.ncbi.nlm.nih.gov/geo/ArrayExpress
MicroArray
http://www.ebi.ac.uk/arrayexpress/
YMGV MicroArray
SMD MicroArray
Marc et al. (2001) http://www.transcriptome.ens.fr/ymgv/
Gollub et al. (2003) http://smd.stanford.edu/
OPD
Mass Spec/Proteomics
Prince et al. (2004)
http://bioinformatics.icmb.utexas.edu/OPD/